Drivers' cognitive load detection through ocular metrics classification for the development on Driver Monitoring Systems

汽车自动化可以减少由于分心等人为因素造成的碰撞次数，从而使道路更安全。然而，尽管在过去十年中技术取得了进步，但完全不需要人类驾驶员输入的车辆尚未得到广泛采用。可用的车辆自动化（SAE 2级和3级系统）；L2/L3)仍然要求人类保持参与驾驶的整体任务，要么保持车辆的安全运行，要么在必要时接管控制权。为了确保满足这些要求，驾驶员监控系统（DMS）正在开发中，其首要目标是监控人类驾驶员的状态。关于DMS提高道路安全的潜力，仍然存在一个问题，即这些系统在L2/L3驾驶过程中如何准确地监测驾驶员的认知负荷，并检测分心等危险情况的发生。因此，本研究旨在开发更准确的DMS，能够检测驾驶员在L2/L3驾驶过程中的认知负荷变化。这项研究有三个不同的目标。目的1将采用基本事实测量来测试非侵入性眼部指标（瞳孔大小、凝视浓度、眨眼频率）在检测驾驶员认知负荷方面的准确性。目标2将使用眼部指标来监测驾驶员在模拟和道路上L2/L3驾驶时认知负荷的变化。目标3将开发和测试分类模型的准确性，以区分L2/L3驾驶期间不同水平的认知负荷（从低到高）。这个项目的影响是双重的。DMS的发展。申请人与参与DMS开发的汽车供应商（Dreyev, Faurecia）有合作关系。本项目收集的数据将用作基准，帮助行业合作伙伴测试其DMS解决方案的可靠性。采用多种视觉指标组合的分类模型也将为更准确的认知负荷检测系统的发展提供信息。L2/L3政策制定。申请人是自动驾驶汽车领域行业组织（ISO/Transport Canada， SAE）的积极参与者，并与安大略省交通部保持积极合作。项目研究结果将被用于制定道路安全政策，以确保安全采用L2/L3自动驾驶系统。新政策的发展和准确的DMS将为所有加拿大人带来更安全的车辆和街道。在这个研究项目中训练的HQP将拥有非常抢手的技能，并能够为学术界、政府或工业界这个快节奏的新兴领域做出贡献。

Vehicle automation can make roads safer by reducing the number of collisions attributed to human factors like distraction. However, despite the technological advancements observed over the last decade, vehicles requiring absolutely no input from the human driver have not yet reached widespread adoption. Available vehicle automation (Society of Automotive Engineers (SAE) level-2 and 3 systems; L2/L3) still requires the human to stay engaged in the overall task of driving either to maintain safe vehicle operations or to take over control whenever necessary. To ensure that these requirements are met, Driver Monitoring Systems (DMS) are being developed with the overarching goal of monitoring the state of the human driver. Relating to the potential for DMS to improve road safety, there remains the question of how accurately these systems can monitor the driver's cognitive load during L2/L3 driving and detect the onset of dangerous conditions like distraction. Thus, this research aims to develop more accurate DMS that are capable of detecting changes in drivers' cognitive load during L2/L3 driving. The research is articulated in three distinct objectives. Objective 1 will adopt ground-truth measures to test the accuracy of non-intrusive ocular metrics (pupil size, gaze concentration, blink rate) in detecting driver's cognitive load. Objective 2 will use ocular metrics to monitor changes in driver's cognitive load during simulated and on-road L2/L3 driving. Objective 3 will develop and test the accuracy of classification models in distinguishing between diverse levels of cognitive load (low to high) during L2/L3 driving. The impact of this project is twofold. DMS development. The applicant has existing collaborations with automotive suppliers (Dreyev, Faurecia) which are involved in the development of DMS. Data collected in this project will be used as a benchmark to help industry partners test the reliability of their DMS solutions. Classification models adopting diverse combinations of ocular metrics will also inform the development of more accurate cognitive load detection systems. L2/L3 policy development. The applicant is an active participant in industry organizations in the area of autonomous vehicles (ISO/Transport Canada, SAE) and has an active collaboration with the Ontario Ministry of Transportation. Project findings will be leveraged to inform the development of road safety policies for the safe adoption of L2/L3 automated driving systems. The development of new policies and accurate DMS will lead to safer vehicles and streets for all Canadians. The HQP trained in this research program will have highly sought-after skills and be able to contribute to this fast-paced emerging sector whether in academia, government, or industry.